"""simple docstring"""






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_A        =     logging.get_logger(__name__)

_A        =     OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

_A        =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Optional[Any]:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												SCREAMING_SNAKE_CASE__			=					model_type_to_module_name(lowerCamelCase_							)

												SCREAMING_SNAKE_CASE__			=					importlib.import_module(F""".{module_name}"""	,       "transformers.models"							)
												try:
																return getattr(lowerCamelCase_	,       lowerCamelCase_							)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowerCamelCase_	,       "__name__"	,       lowerCamelCase_							) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				SCREAMING_SNAKE_CASE__			=					importlib.import_module("transformers"							)
				if hasattr(lowerCamelCase_	,       lowerCamelCase_							):
								return getattr(lowerCamelCase_	,       lowerCamelCase_							)

				return None




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase = None	,       __UpperCAmelCase = False	,       __UpperCAmelCase = False	,       __UpperCAmelCase = None	,       __UpperCAmelCase = None	,       __UpperCAmelCase = None	,       __UpperCAmelCase = False	,       **__UpperCAmelCase	,       )				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					get_file_from_repo(
				    lowerCamelCase_	,       lowerCamelCase_	,       cache_dir=lowerCamelCase_	,       force_download=lowerCamelCase_	,       resume_download=lowerCamelCase_	,       proxies=lowerCamelCase_	,       use_auth_token=lowerCamelCase_	,       revision=lowerCamelCase_	,       local_files_only=lowerCamelCase_	,       )
				if resolved_config_file is None:
								logger.info(
								    "Could not locate the image processor configuration file, will try to use the model config instead."							)
								return {}

				with open(lowerCamelCase_	,       encoding="utf-8"							) as reader:
								return json.load(lowerCamelCase_							)

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(    self      :					Optional[Any]		)				->							int:
									raise EnvironmentError(
									    "AutoImageProcessor is designed to be instantiated "
									    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."		)



					@classmethod
					@replace_list_option_in_docstrings(__UpperCAmelCase		)
					def       lowerCAmelCase_       (    cls      :					List[Any] ,			_snake_case      :					Tuple ,			**_snake_case      :					List[Any]		)				->							Tuple:
									SCREAMING_SNAKE_CASE__			=					kwargs.pop("config" ,			__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					kwargs.pop("trust_remote_code" ,			__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					True

									SCREAMING_SNAKE_CASE__			=					ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase ,			**__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					config_dict.get("image_processor_type" ,			__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					None
									if "AutoImageProcessor" in config_dict.get("auto_map" ,			{}		):
													SCREAMING_SNAKE_CASE__			=					config_dict['''auto_map''']['''AutoImageProcessor''']

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
													SCREAMING_SNAKE_CASE__			=					config_dict.pop("feature_extractor_type" ,			__UpperCAmelCase		)
													if feature_extractor_class is not None:
																	logger.warning(
																	    "Could not find image processor class in the image processor config or the model config. Loading"
																	    " based on pattern matching with the model\'s feature extractor configuration."		)
																	SCREAMING_SNAKE_CASE__			=					feature_extractor_class.replace("FeatureExtractor" ,			"ImageProcessor"		)
													if "AutoFeatureExtractor" in config_dict.get("auto_map" ,			{}		):
																	SCREAMING_SNAKE_CASE__			=					config_dict['''auto_map''']['''AutoFeatureExtractor''']
																	SCREAMING_SNAKE_CASE__			=					feature_extractor_auto_map.replace("FeatureExtractor" ,			"ImageProcessor"		)
																	logger.warning(
																	    "Could not find image processor auto map in the image processor config or the model config."
																	    " Loading based on pattern matching with the model\'s feature extractor configuration."		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
													if not isinstance(__UpperCAmelCase ,			__UpperCAmelCase		):
																	SCREAMING_SNAKE_CASE__			=					AutoConfig.from_pretrained(__UpperCAmelCase ,			**__UpperCAmelCase		)
													# It could be in `config.image_processor_type``
													SCREAMING_SNAKE_CASE__			=					getattr(__UpperCAmelCase ,			"image_processor_type" ,			__UpperCAmelCase		)
													if hasattr(__UpperCAmelCase ,			"auto_map"		) and "AutoImageProcessor" in config.auto_map:
																	SCREAMING_SNAKE_CASE__			=					config.auto_map['''AutoImageProcessor''']

									if image_processor_class is not None:
													SCREAMING_SNAKE_CASE__			=					image_processor_class_from_name(__UpperCAmelCase		)

									SCREAMING_SNAKE_CASE__			=					image_processor_auto_map is not None
									SCREAMING_SNAKE_CASE__			=					image_processor_class is not None or type(__UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING
									SCREAMING_SNAKE_CASE__			=					resolve_trust_remote_code(
									    __UpperCAmelCase ,			__UpperCAmelCase ,			__UpperCAmelCase ,			__UpperCAmelCase		)

									if has_remote_code and trust_remote_code:
													SCREAMING_SNAKE_CASE__			=					get_class_from_dynamic_module(
													    __UpperCAmelCase ,			__UpperCAmelCase ,			**__UpperCAmelCase		)
													SCREAMING_SNAKE_CASE__			=					kwargs.pop("code_revision" ,			__UpperCAmelCase		)
													if os.path.isdir(__UpperCAmelCase		):
																	image_processor_class.register_for_auto_class()
													return image_processor_class.from_dict(__UpperCAmelCase ,			**__UpperCAmelCase		)
									elif image_processor_class is not None:
													return image_processor_class.from_dict(__UpperCAmelCase ,			**__UpperCAmelCase		)
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(__UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING:
													SCREAMING_SNAKE_CASE__			=					IMAGE_PROCESSOR_MAPPING[type(__UpperCAmelCase		)]
													return image_processor_class.from_dict(__UpperCAmelCase ,			**__UpperCAmelCase		)

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}"""		)



					@staticmethod
					def       lowerCAmelCase_       (    _snake_case      :					Tuple ,			_snake_case      :					int		)				->							Any:
									IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase ,			__UpperCAmelCase		)







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[int]								=	StableDiffusionInstructPixaPixPipeline
							UpperCAmelCase_						:		str								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
							UpperCAmelCase_						:		List[Any]								=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCAmelCase_						:		Optional[int]								=	IMAGE_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase_						:		Any								=	IMAGE_TO_IMAGE_IMAGE_PARAMS




							def       snake_case			(					self						)		->       List[Any]:
														torch.manual_seed(0						)
														A      :					Tuple      		=					UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=8    ,						out_channels=4    ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,						cross_attention_dim=32    ,						)
														A      :					Any      		=					PNDMScheduler(skip_prk_steps=__UpperCAmelCase						)
														torch.manual_seed(0						)
														A      :					Dict      		=					AutoencoderKL(
														    block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,						latent_channels=4    ,						)
														torch.manual_seed(0						)
														A      :					str      		=					CLIPTextConfig(
														    bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=10_00    ,						)
														A      :					Dict      		=					CLIPTextModel(__UpperCAmelCase						)
														A      :					Dict      		=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

														A      :					int      		=					{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Dict:
														A      :					Any      		=					floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Dict      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
														A      :					Optional[Any]      		=					Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						)
														if str(__UpperCAmelCase						).startswith('''mps'''						):
																					A      :					str      		=					torch.manual_seed(__UpperCAmelCase						)
														else:
																					A      :					Optional[Any]      		=					torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
														A      :					List[Any]      		=					{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''image_guidance_scale''': 1,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Optional[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Dict      		=					self.get_dummy_components()
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Optional[Any]      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					List[str]      		=					image[0, -3:, -3:, -1]
														assert image.shape == (1, 32, 32, 3)
														A      :					Optional[Any]      		=					np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       int:
														A      :					Any      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Optional[Any]      		=					self.get_dummy_components()
														A      :					Any      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					str      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[int]      		=					'''french fries'''
														A      :					Any      		=					sd_pipe(**__UpperCAmelCase    ,						negative_prompt=__UpperCAmelCase						)
														A      :					Optional[int]      		=					output.images
														A      :					str      		=					image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														A      :					Any      		=					np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Tuple:
														A      :					List[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Optional[int]      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					[inputs['''prompt''']] * 2

														A      :					List[str]      		=					np.array(inputs['''image''']						).astype(np.floataa						) / 2_5_5.0
														A      :					Any      		=					torch.from_numpy(__UpperCAmelCase						).unsqueeze(0						).to(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					image / 2 + 0.5
														A      :					str      		=					image.permute(0    ,						3    ,						1    ,						2						)
														A      :					List[str]      		=					image.repeat(2    ,						1    ,						1    ,						1						)

														A      :					Union[str, Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					int      		=					image[-1, -3:, -3:, -1]

														assert image.shape == (2, 32, 32, 3)
														A      :					Any      		=					np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					int      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Union[str, Any]      		=					EulerAncestralDiscreteScheduler(
														    beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						beta_schedule='''scaled_linear'''						)
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Tuple      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					Dict      		=					image[0, -3:, -3:, -1]

														A      :					Optional[int]      		=					[round(__UpperCAmelCase    ,						4						) for x in image_slice.flatten().tolist()]
														print(''','''.join([str(__UpperCAmelCase						) for x in slice]						)						)

														assert image.shape == (1, 32, 32, 3)
														A      :					Dict      		=					np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Union[str, Any]:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					int      		=					self.get_dummy_components()
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					VaeImageProcessor(do_resize=__UpperCAmelCase    ,						do_normalize=__UpperCAmelCase						)
														A      :					Optional[Any]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					pipe(**self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)						)[0]

														A      :					Optional[Any]      		=					components['''vae''']
														A      :					Dict      		=					self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)

														for image_param in self.image_latents_params:
																					if image_param in inputs.keys():
																												A      :					Optional[Any]      		=					vae.encode(inputs[image_param]						).latent_dist.mode()

														A      :					List[str]      		=					pipe(**__UpperCAmelCase						)[0]

														A      :					Any      		=					np.abs(out - out_latents_inputs						).max()
														self.assertLess(__UpperCAmelCase    ,						1E-4    ,						'''passing latents as image input generate different result from passing image'''						)







@slow
@require_torch_gpu
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def       snake_case			(					self    ,						__UpperCAmelCase=0						)		->       Tuple:
														A      :					List[str]      		=					torch.manual_seed(__UpperCAmelCase						)
														A      :					List[str]      		=					load_image(
														    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''						)
														A      :					Dict      		=					{
														    '''prompt''': '''turn him into a cyborg''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 3,
														    '''guidance_scale''': 7.5,
														    '''image_guidance_scale''': 1.0,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Optional[int]      		=					self.get_inputs()
														A      :					Any      		=					pipe(**__UpperCAmelCase						).images
														A      :					Union[str, Any]      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Union[str, Any]      		=					self.get_inputs()
														A      :					Union[str, Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       List[Any]:
														A      :					List[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					DDIMScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					str      		=					self.get_inputs()
														A      :					List[Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Any      		=					np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					0

														def callback_fn(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						) -> None:
																					A      :					List[Any]      		=					True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												A      :					Optional[Any]      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Union[str, Any]      		=					latents[0, -3:, -3:, -1]
																												A      :					List[str]      		=					np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																					elif step == 2:
																												A      :					int      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Optional[int]      		=					latents[0, -3:, -3:, -1]
																												A      :					str      		=					np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2

														A      :					Optional[Any]      		=					False

														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					List[str]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					List[Any]      		=					self.get_inputs()
														pipe(**__UpperCAmelCase    ,						callback=__UpperCAmelCase    ,						callback_steps=1						)
														assert callback_fn.has_been_called
														assert number_of_steps == 3




							def       snake_case			(					self						)		->       Tuple:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														A      :					Optional[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					Dict      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing(1						)
														pipe.enable_sequential_cpu_offload()

														A      :					Optional[Any]      		=					self.get_inputs()
														A      :					str      		=					pipe(**__UpperCAmelCase						)

														A      :					Dict      		=					torch.cuda.max_memory_allocated()
														# make sure that less than 2.2 GB is allocated
														assert mem_bytes < 2.2 * 10**9






							def       snake_case			(					self						)		->       int:
														A      :					Union[str, Any]      		=					self.get_inputs()
														# resize to resolution that is divisible by 8 but not 16 or 32
														A      :					Union[str, Any]      		=					inputs['''image'''].resize((5_04, 5_04)						)

														A      :					str      		=					'''timbrooks/instruct-pix2pix'''
														A      :					Tuple      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    __UpperCAmelCase    ,						safety_checker=__UpperCAmelCase    ,						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Tuple      		=					pipe(**__UpperCAmelCase						)
														A      :					List[str]      		=					output.images[0]

														A      :					List[Any]      		=					image[2_55:2_58, 3_83:3_86, -1]

														assert image.shape == (5_04, 5_04, 3)
														A      :					Any      		=					np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-3






